Image quality assessment method and device

Abstract

The invention relates to an image quality assessment method and an image quality assessment device. The method comprises the steps of acquiring a predetermined quantity of sample images, and extracting preset feature values in each sample image related to Gabor wavelet transform information, YCbCr color spatial feature information and MSCN coefficient statistical feature information of the sample image; training by using a support vector machine (SVM) method to acquire a SVM classifier according to the extracted preset feature values in each sample image; and receiving an input image, and assessing the input image according to the SVM classifier to acquire an assessment result. According to the method and the device provided by the invention, the visual perception effect of people on image quality can be accurately reflected, and image quality assessment prediction precision and accuracy can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, and in particular, to an image quality assessment method and an image quality assessment device. Background technique [0002] With the advent of the information age, digital images have become very common as an important information carrier. For example, digital images of some bills, ID cards, customer information, etc. have become commonly used information carriers in social activities. With the development of digital image signal processing technology, image engineering has become a subject with rich content and rapid development. An image processing system usually includes image acquisition, display, storage, communication, processing and analysis. Under the current technical level, some distortion will inevitably occur in the process of image acquisition, transmission and processing. However, in many applications, it is necessary to quantify how the distortion in the image...

AI Technical Summary

Problems solved by technology

However, in related technologies, some current non-reference image quality assessment algorithms still have some shortcomings. For example, most algorithms use the local deformation characteristics of blurred images to predict image quality, which obviously has great limitations.

In addition, most feature extraction processes for images only feature a single color model, which is difficult to accurately reflect the human visual perception of image quality. In practical applications, the prediction accuracy of image quality evaluation and the evaluation and prediction effect need to be improved.

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